The uncertainty with using risk prediction models to drive clinical decision making

Student thesis: Phd


Risk prediction models have become embedded into the health system. They are used to guide clinical decision making in a variety of settings: risk of death following surgery (should we operate?), diagnostic models for cancer (should we screen?), or the probability of having a clinical event over a certain time period (should we take preventative measures?). Despite clear guidelines on the development and reporting of models, features of models developed for the same purpose often differ. Furthermore, in the field of cardiovascular disease (CVD), risk thresholds for initiating statin therapy vary across England, Scotland, the US and Europe, despite a large body of evidence on when treatment becomes cost effective. This results in uncertainty when using these models to guide treatment for a patient, as using different models or clinical guidelines may result in a different decision for an individual. This thesis focused on identifying sources of uncertainty associated with both parts of this process, generating risk predictions, and making clinical decisions based on these risk predictions. Case studies consider the primary prevention of CVD, which was chosen due to the high incidence of CVD, the saturation of CVD risk prediction models in the literature, and the fierce debate over the last 10 years about the best approach for the primary prevention of CVD. Chapter 3 found the impact of covariate selection on the risks of individuals to be small, apart from a large secular trend. Chapter 4 identified high levels of instability in risk scores when using sample sizes of widely used models, and when derived from recently published sample size formula. Chapter 5 found that the secular trend in CVD (identified in Chapter 3) caused over prediction of risks for patients in the present day and was not driven by increasing statin use. Chapter 6 highlighted that a small number of extra CVD events could be prevented by delaying statin initiation to when patients are at risks higher than 10% (given the high statin discontinuation rates identified in practice). Chapter 7 showed that the reduction of the risk threshold for initiating statins for the primary prevention of CVD, from a 10-year CVD risk of 20% to 10%, had little impact on clinical practice in England. This finding is contrary to current evidence. The findings in this thesis are a mix of methodological findings of interest to those developing models, and those that have a direct impact on the prevention of CVD in the UK.
Date of Award1 Aug 2020
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorRichard Emsley (Supervisor) & Tjeerd Van Staa (Supervisor)


  • risk prediction
  • cardiovascular disease
  • uncertainty
  • stability
  • individual risk

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